This book introduces a revolutionary framework for knowledge representation and AI agent memory: Semantic Spacetime. Drawing from theoretical physics and graph theory, this framework offers a new way to understand how meaning, relationships, and causality can be structured in intelligent systems.

Why This Book Matters

Current approaches to AI memory and knowledge representation face fundamental limitations. Vector embeddings, while popular, create opaque high-dimensional spaces where relationships lack clear semantic meaning. Traditional graph databases often rely on arbitrary relationship types that don't generalize across domains. Most critically, existing systems struggle with the dynamic, contextual nature of how humans actually understand and use knowledge.

Semantic Spacetime addresses these challenges by proposing four fundamental relationship types—NEAR/SIMILAR TO, LEADS TO, CONTAINS, and EXPRESSES PROPERTY—that can represent virtually any knowledge domain while maintaining semantic clarity and computational tractability.

What You'll Discover

This book explores how spatial and temporal concepts from physics can be adapted to create semantic spaces where meaning emerges from relationships. You'll learn how causality graphs can form the backbone of AI agent memory, enabling systems that don't just store information but understand the "why" behind events and decisions.

The framework presented here moves beyond static knowledge representation to embrace the dynamic, contextual nature of understanding. By focusing on causal relationships and pragmatic proximity, AI systems can adapt their knowledge structures to different contexts and purposes, much like human cognition.

For Whom This Book Is Written

This book is intended for researchers and practitioners working in AI, knowledge representation, graph databases, and semantic technologies. While the concepts are rigorous, they are presented with practical applications and implementation considerations in mind.

Whether you're building recommendation systems, developing AI agents for personal assistance, creating knowledge management platforms, or exploring the foundations of machine reasoning, the principles in this book provide both theoretical grounding and practical guidance.

The Journey Ahead

The framework presented here represents a synthesis of ideas from multiple disciplines: graph theory, category theory, physics, cognitive science, and computer science. By bringing these perspectives together, we can build AI systems that not only process information but truly understand the structured nature of knowledge and experience.

This is not just another approach to knowledge representation—it's a fundamental rethinking of how intelligent systems can model the world in ways that align with how humans actually think and reason about complex relationships and causality.

When we started building AI memory for the personal assistant agent, it was quite straightforward. We used knowledge graphs, and everything was heavy, and it was super easy. You could find a lot of articles on how to turn conversations into graphs. But we noticed quite quickly that after a couple of weeks of the honeymoon period, users started giving us bad feedback, and we also noticed that something wasn't working in the memory.

This non-working component was actually time awareness, because the LLM or the agent didn't know what happens when, and whether it was relevant information or not. Memory is actually quite complex and temporal, and we differentiate between past and future and what's more important.

So this book is actually about time and temporal awareness in memory—how to make the LLM and agent understand time, and how to actually make knowledge graphs temporal. Yes, exactly—knowledge graphs, because knowledge graphs actually matter and they are the core of memory. You'll also learn about events and how to build proper episodic memory, why events are important, and together with time, we'll talk about causality and why causality relations are so important for the agent primarily. So it's a book about causality and time.

This book introduces a revolutionary framework for knowledge representation and AI agent memory: Semantic Spacetime. Drawing from theoretical physics and graph theory, this framework offers a new way to understand how meaning, relationships, and causality can be structured in intelligent systems.

Why This Book Matters

Current approaches to AI memory and knowledge representation face fundamental limitations. Vector embeddings, while popular, create opaque high-dimensional spaces where relationships lack clear semantic meaning. Traditional graph databases often rely on arbitrary relationship types that don't generalize across domains. Most critically, existing systems struggle with the dynamic, contextual nature of how humans actually understand and use knowledge.

Semantic Spacetime addresses these challenges by proposing four fundamental relationship types—NEAR/SIMILAR TO, LEADS TO, CONTAINS, and EXPRESSES PROPERTY—that can represent virtually any knowledge domain while maintaining semantic clarity and computational tractability.

What You'll Discover

This book explores how spatial and temporal concepts from physics can be adapted to create semantic spaces where meaning emerges from relationships. You'll learn how causality graphs can form the backbone of AI agent memory, enabling systems that don't just store information but understand the "why" behind events and decisions.

The framework presented here moves beyond static knowledge representation to embrace the dynamic, contextual nature of understanding. By focusing on causal relationships and pragmatic proximity, AI systems can adapt their knowledge structures to different contexts and purposes, much like human cognition.

For Whom This Book Is Written

This book is intended for researchers and practitioners working in AI, knowledge representation, graph databases, and semantic technologies. While the concepts are rigorous, they are presented with practical applications and implementation considerations in mind.

Whether you're building recommendation systems, developing AI agents for personal assistance, creating knowledge management platforms, or exploring the foundations of machine reasoning, the principles in this book provide both theoretical grounding and practical guidance.

The Journey Ahead

The framework presented here represents a synthesis of ideas from multiple disciplines: graph theory, category theory, physics, cognitive science, and computer science. By bringing these perspectives together, we can build AI systems that not only process information but truly understand the structured nature of knowledge and experience.

This is not just another approach to knowledge representation—it's a fundamental rethinking of how intelligent systems can model the world in ways that align with how humans actually think and reason about complex relationships and causality.

If you're following my work, I have a couple of deep research pieces about agentic memory and the application of spacetime concepts. Right now, I'm also working on a book about agentic protocols. This particular book focuses on the question of better context for agents—following the ideas of context graphs and exploring how to actually build something beyond context graphs to manage decision traces.

The goal is to create an architecture for enterprise-level agents that follow rules, make their own decisions, and—most importantly—make explainable decisions. I'll also explore the ability for agents to learn and apply that learning based on past experience.

We'll talk extensively about concepts like agentic memory: why we need memory, how to build memory, and why memory is not just another RAG system. We'll cover how to apply decision traces, how they work, and why cognitive processes—just like memory structures—contribute to learning capabilities.

Beyond this, we'll explore promise theory and promise graphs as an extension of agentic action logs. This creates a rich trace from data signals to promises to actions, making the architecture multi-agent ready. We'll examine this in the scope of agent cooperation, where agents don't just act in isolation but coordinate through explicit promises and commitments.

My core assumption with this book is that we need to build sophisticated agentic memory that goes beyond context graphs, beyond knowledge graphs, and applies advanced topics like causality, temporal causality research, and a deep focus on time itself. This is extremely relevant to my memory book, but here the focus is specifically on agents—and how they work together.

If you're following my work, I have a couple of deep research pieces about agentic memory and the application of spacetime concepts. Right now, I'm also working on a book about agentic protocols. This particular book focuses on the question of better context for agents—following the ideas of context graphs and exploring how to actually build something beyond context graphs to manage decision traces.

The goal is to create an architecture for enterprise-level agents that follow rules, make their own decisions, and—most importantly—make explainable decisions. I'll also explore the ability for agents to learn and apply that learning based on past experience.

We'll talk extensively about concepts like agentic memory: why we need memory, how to build memory, and why memory is not just another RAG system. We'll cover how to apply decision traces, how they work, and why cognitive processes—just like memory structures—contribute to learning capabilities.

Beyond this, we'll explore promise theory and promise graphs as an extension of agentic action logs. This creates a rich trace from data signals to promises to actions, making the architecture multi-agent ready. We'll examine this in the scope of agent cooperation, where agents don't just act in isolation but coordinate through explicit promises and commitments.

My core assumption with this book is that we need to build sophisticated agentic memory that goes beyond context graphs, beyond knowledge graphs, and applies advanced topics like causality, temporal causality research, and a deep focus on time itself. This is extremely relevant to my memory book, but here the focus is specifically on agents—and how they work together.